mlsanityData to 7 Oct 2026

A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts

arXiv 2408.08242 ↗First posted 15 Aug 2024cs.RO cs.AI cs.LGcited by 1

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs' environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving efficiency. Moreover, model predictive control ensures stability and precision in execution. Experimental results demonstrate that the proposed system consistently outperforms state-of-the-art methods, achieving fewer collisions, reduced travel time, and stable training with smooth reward convergence.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 12 of its 43 references that are in the map. First posted on arXiv 15 Aug 2024.

Builds on

12 of 43 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks11 Aug

Other references · 11

  1. 2024KAN: Kolmogorov-Arnold Networkscited by 634
  2. 2019Self-Driving Cars: A Surveycited by 147
  3. 2024KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Searchcited by 36
  4. 2020An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviorscited by 16
  5. 2019Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validationcited by 8
  6. 2023Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approachcited by 6
  7. 2024Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learningcited by 4
  8. 2021Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approachcited by 3
  9. 2023Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approachcited by 2
  10. 2023Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximatorcited by 2
  11. 2023Control of Cross-Directional Systems with Approximate Symmetriescited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Surveycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning
  2. 2024Queue-based Eco-Driving at Roundabouts with Reinforcement Learning
  3. 2024Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions
  4. 2025Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
  5. 2026Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
  6. 2024Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
  7. 2023Deep Q-Network Based Decision Making for Autonomous Driving
  8. 2025BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios